Science Alert July 11, 2022
Current artificial intelligence systems pale in their understanding of intuitive physics, in comparison to even very young children. An international team of researchers (USA – Princeton University, UK) addressed this gap between humans and machines by drawing on the field of developmental psychology. They introduced and open-sourced a machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology. Then they built a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children. They demonstrated that their model could learn a diverse set of physical concepts, which depends critically on object-level representations, consistent with findings from developmental psychology. They considered the implications of these results both for AI and for research on human cognition… read more. Open Access TECHNICAL ARTICLEÂ
PLATO displays robust effects with as little as 28 h of visual experience. Credit: Nature Human Behaviour (2022)